Facial expression recognition through modeling age-related spatial patterns

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• 作者：Shangfei Wang ; Shan Wu ; Zhen Gao ; Qiang Ji
• 关键词：Expression recognition ; Age ; related spatial patterns ; Privileged information ; Bayesian networks
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：75
• 期：7
• 页码：3937-3954
• 全文大小：1,179 KB
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• 作者单位：Shangfei Wang (1)
  Shan Wu (1)
  Zhen Gao (1)
  Qiang Ji (2)
  
  1. Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, 230027, People’s Republic of China
  2. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute Troy, Troy, NY, 12180, USA
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

In this paper we tackle the problem of expression recognition by exploiting age-related spatial facial expression patterns, which carry crucial information that have not been thoroughly exploited. First, we conduct two statistic hypothesis tests to investigate age effect on the spatial patterns of expressions and on facial expression recognition respectively. Second, we propose two methods to recognize expressions by modeling age-related spatial facial expression patterns. One is a three-node Bayesian Network to classify expressions with the help of age from person-independent geometric features. The other is to construct multiple Bayesian networks to explicitly capture the spatial facial expression patterns for different ages. For both methods, age information is used as privileged information, which is only available during training, and is exploited during training to construct a better classifier. Statistic analyses on two benchmark databases, i.e. the Lifespan and the FACES, verify the age effect on spatial patterns of expressions and on facial expression recognition. Experimental results of expression recognition demonstrate the effectiveness of the proposed methods in modelling age-related spatial patterns as well as their superior expression recognition performance to existing approaches.